package org.example

import org.apache.spark.sql.SparkSession

object lanxi1 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().master("local[*]").appName("spark").getOrCreate()
    val sc = spark.sparkContext
    val  first_half =sc.textFile("E:\\spark23\\Employee_salary_first_half.csv")
    val  second_half =sc.textFile("E:\\spark23\\Employee_salary_second_half.csv")
    val drop_first =first_half.mapPartitionsWithIndex((ix,it) =>{
      if (ix ==0) it.drop(1)
      else it
    })
    val drop_second =second_half.mapPartitionsWithIndex((ix,it) =>{
      if (ix == 0) it.drop(1)
       else it
    })
    val split_first=drop_first.map(
      Line =>{val data =Line.split(",");(data(1),data(6).toInt)})
    val split_second=drop_second.map(
      Line =>{val data =Line.split(",");(data(1),data(6).toInt)})
    val filter_first=split_first.filter(x => x._2 > 200000).map(x => x._1)
    val filter_second=split_second.filter(x => x._2 > 200000).map(x => x._1)
    val name=filter_first.union(filter_second).distinct()
    name.collect().foreach(println)
    val salary = split_first.union(split_second)
    val avg_salary = salary.combineByKey(
      count => (count, 0),
      (acc:(Int, Int), count) => (acc._1 + count, acc._2 + 0),
      (acc1:(Int, Int), acc2:(Int, Int)) => (acc1._1 + acc2._1, acc1._2 + acc2._2)
    )
    avg_salary.map(x => (x._1, x._2._1.toDouble / 12)).foreach(println)
    val total = split_first.join(split_second).join(salary).join(avg_salary).map(
    x => Array(x._1, x._2._1._1._1, x._2._1._1._2,
    x._2._1._2, x._2._2).mkString(
    ","))
    total.repartition(1).saveAsTextFile("C:\\Users\\Administrator\\Desktop\\save")
  }

}
